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Generate a structured bug report for GitHub issues based on conversation context about bugs, errors, or broken functionality
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Validate review findings about an implementation plan against the codebase and repo rules, fix the valid ones in the plan, and write a feedback file noting what changed and what was rejected (and why). For plan-review findings, not code-review findings.
Expert review of an implementation plan against a GitHub issue/enhancement description (provided as a local file or a GitHub issue URL) and the current repository codebase. Use when asked to critique a plan for correctness, completeness, codebase alignment, risks, and test/rollout readiness (do not implement).
Manually review a feedback file produced after implementation-plan review findings were addressed. Use when the user asks to read a findings-feedback file and verify that the plan changes correctly close, correctly reject, or fail to close the original implementation-plan-review findings, while checking for newly introduced or newly exposed plan problems. Writes only new findings to the plan-id findings directory and replies with the output file path or a concise no-new-findings status.
Use this skill whenever the agent needs to deploy a smart contract to Sepolia testnet via Foundry (`forge create` or `forge script --broadcast`), routed through the Blockscout PRO API's JSON-RPC gateway instead of a third-party RPC provider. Trigger on requests like "deploy this contract to Sepolia", "deploy via Foundry", "broadcast this Foundry deployment script", or any `forge create`/`forge script` invocation that needs a signer and an RPC endpoint for Sepolia (chain id 11155111) in this project. Also consult it to find which local Foundry keystore to sign with — this project stores test signing keys as encrypted keystores under `.ai/wallet/`, never as a plain private key in `.env`. Do not use this for reads (balances, transaction history, contract info) — use the connected Blockscout MCP tools for those directly; this skill is specifically about broadcasting a deployment.
Enter the worktree of a GitHub PR, validate the code-review comments raised on it against what the code actually does and the repo rules, fix the valid ones directly in the PR's source/tests, and report in chat — a brief note per closed comment and a detailed plain-language explanation of every comment left unclosed and why. For code-review comments on a PR. Invoke manually.
TRIGGER CONDITIONS - invoke this skill when ANY of these apply: User-triggered (explicit request): - User mentions "specification", "SPEC", "SPEC.md", "spec requirements" - User asks to "verify with spec", "confirm with specification", "check requirements" - User requests "authoritative requirements" or "source of truth" Agent self-triggered (during reasoning): - Before implementing any feature or making code changes - When verifying technical requirements or constraints - When making architectural or design decisions - When you need unbiased guidance not influenced by current implementation - When encountering conflicting information and need ground truth PURPOSE: Consults SPEC.md through an isolated subagent that ONLY reads the specification, never implementation code. This ensures guidance is based on authoritative requirements, not potentially incorrect existing code.
| name | gh-issue-bug |
| description | Generate a structured bug report for GitHub issues based on conversation context about bugs, errors, or broken functionality |
| disable-model-invocation | true |
This skill generates a well-structured bug report for GitHub issues based on the conversation context and saves it to .ai/gh_issues/YYMMDD-<short-issue-name>.md. This skill is specifically designed for bug reports - not for feature requests, enhancements, or improvements.
When invoked, this skill extracts bug information from the current conversation and creates a standardized GitHub bug report document.
Use this skill for:
Do NOT use this skill for:
Review the conversation to identify:
Create a filename using the pattern:
.ai/gh_issues/YYMMDD-<short-issue-name-with-dashes>.md
Where:
YYMMDD: Current date in 2-digit year, month, day format (e.g., 260127 for January 27, 2026)<short-issue-name-with-dashes>: A brief, descriptive name using lowercase letters and dashes (e.g., nft-pagination-bug, timeout-error-in-ens-lookup)Example: .ai/gh_issues/260127-nft-pagination-bug.md
Write a Markdown document with the following sections:
# [Brief Title of the Issue]
## Description
[Short description of the bug - 2-3 sentences summarizing the problem]
## Steps to Reproduce
1. [First step]
2. [Second step]
3. [Third step]
...
## Expected Behavior
[Clear description of what should happen]
## Actual Behavior
[Clear description of what actually happens, including any error messages]
## Root Cause
[Technical explanation of why the bug occurs - reference specific code, logic, or system behavior]
## Suggested Fix
[Concise description of the proposed solution without code snippets - describe the approach at a high level]
MUST INCLUDE:
MUST NOT INCLUDE:
After creating the file, confirm:
When the user says:
/gh-issue-bug
You should:
.ai/gh_issues/260127-transaction-logs-timeout.md